For the computation of depth information from a set of two (or even more) images, a matching process is applied to find point correspondences between input images. The displacement between two corresponding points is referred to as disparity. The 3D structure, i.e. the depth, of a scene can be reconstructed from these disparities. Often the performance of the matching process inherently depends on the underlying image content.
A good trade-off for an efficient matching processing needs to be found between the complete exploration of the full range of possible disparity values, which can lead to a significantly high number of candidates to be tested, and a too restrictive limitation of search candidates relying solely on the propagation of good results.
A disadvantages of a full search approach is the significantly high number of candidates to be tested. This results in a high processing load. Moreover, a full search can also lead to noisier disparity maps if, for example, the support window is too small. Despite these drawbacks a full search approach offers the maximum level of inherent parallelism.
On the other side of the spectrum, a too strict limitation of search candidates can lead to disparity maps lacking fine details. Moreover, the propagation of results within one image can significantly limit the inherent parallelism. In the extreme case, only a sequential processing is possible. Therefore, as mentioned above a good trade-off between the two approaches has to be found for a fast and efficient parallel processing approach.